Ch 1 Introduction to AI Applications.pdf

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1. 1
ARTIFICIAL INTELLIGENCE
Introduction: Chapter 1
يعانطصلإا ءاكذلا

2. Howwouldyoudefine“intelligence”?
Whatisthecommondefinitionof“AI”?
WhataretheAIsub-topics?Whichtopics
failed?successful?Why?
DoyouknowanyAIrealapplication?
Shouldartificialintelligencesimulatenatural
intelligence?
WhatistherelationbetweenAIandlogic?
Doyouthinkthatcomputersormachineswill
everbeasintelligentashumans?
Whatisthemainadvantageofcomputers
overpeopleandviceversa?
HowfarisAIfromreachinghuman-level
intelligence?Whenwillithappen.
Arecomputersfastenoughtobeintelligent?
DoyoudefinitelyagreethatAIaugmenting
humancapabilityandcapacity,oritwill
damagethehumanlife?

4. 2021
2005

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What is Intelligent
Therearemanydefinitionsofintelligence.
Apersonthatlearnsfastoronethathasavast
amountofexperience,couldbecalled
"intelligent".
Howeverforourpurposesthemostusefuldefinition
is:systemscomparativelevelofperformancein
reachingitsobjectives
personsarenotintelligentinallareasofknowledge,theyareonly
intelligentinthoseareaswheretheyhadexperiences.

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AI Goals
•ArtificialIntelligentisthepartofcomputersciencewithdesigning
intelligentcomputersystems,thatis,systemsthathave
characteristicsassociatewithintelligenceinhumanbehaviour–
understandinglanguage,learning,reasoning,solving
problems………………
•Scientific GoalTo determine which ideas about knowledge
representation, learning, rule systems, search, and so on, explain
various sorts of real intelligence.
•Engineering GoalTo solve real world problems using AI
techniques such as..
knowledge representation, learning, rule systems, search, and so
on.

7. Artificial Intelligence in the Movies

8. Why the interest in AI?
Search engines
Labor
Science
Medicine/
Diagnosis
Appliances What else?

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What is AI?
ViewsofAIfallintofourcategories:
•Thinkinghumanly:systemsthatthinkslikehumans,(machine
withmind).Activitiesasdecision-making,problemsolving,
learning,……
•Thinkingrationally:thestudyofthinkingfaculties.
•Actinghumanly:systemsthatactinglikehumans,thestudyof
howtomakecomputersdothings.
•Actingrationally:Thestudyofdesigningintelligentagents
Thetextbookadvocates“ActingRationally"

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Systems that
thinklike humans
Systems that
thinkrationally
Systems that act
like humans
Systems that act
rationally
Turing test
Cognitive
science
Logic
Agents

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How do Humans do Intelligent Things?
•It seems natural to try to base our AI systems on the human nervous system.
This can be broken down into three stages that may be represented in block
diagram form as:
Receptors collect information from the environment, and effectors generate
interactions with the environment. The flow of information between them is
represented by arrows
–both forward and backward.
What we generally describe as “intelligence” is normally carried out in the central
stage
–in the brain. The brain is known to consist of an interconnected network of
neurons, and the study of neural networks is now a major sub-field of AI.

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IS Pilot Architecture

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Acting Humanly: Turing Test
•Turing(1950)"Computingmachineryandintelligence":
•"Canmachinesthink?"→"Canmachinesbehaveintelligently?"
•Operationaltestforintelligentbehavior:theImitationGame
•AlanTuring'sdiscussedtheconditionsforconsideringamachinetobe
intelligent.Hediscussesthatifthemachinecouldsuccessfullypretendtobe
humantoaknowledgeableobserverthenyoucertainlyshouldconsiderit
intelligent.
•Askquestionsoftwoentities,andreceiveanswersfromboth
•Ifyoucan’ttellwhichoftheentitiesishumanandwhichisacomputer
program,thenweshouldthereforeconsiderthecomputertobeintelligent

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Sub-fields of Artificial Intelligence
AI now consists many sub-fields, using a variety of techniques, such as:
Neural Networks–e.g. brain modeling, time series prediction,
classification
Evolutionary Computation –e.g. genetic algorithms, genetic
programming
Computer Vision –e.g. object recognition, image understanding
Robotics–e.g. intelligent control, autonomous exploration
Expert Systems –e.g. decision support systems, teaching
systems
Speech Processing–e.g. speech recognition and production
Natural Language Processing –e.g. machine translation
Machine Learning –e.g. decision tree learning, version space
learning
Most of these have both engineering and scientific aspects.

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Rational agents
•Anagentisanentitythatperceivesand
acts
•Thiscourseisaboutdesigningrational
agents
•anagentisafunctionfrompercept
historiestoactions:
[ f : P* →A]
•Foranygivenclassofenvironmentsand
tasks,weseektheagent(orclassof
agents)withthebestperformance
•Note:Computationallimitationsmake
perfectrationalityunachievable
→Designthebestprogramforgiven
machineresources

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Examples of AI Agents
Humans Programs Robots___
senses keyboard, mouse, dataset cameras, pads
body parts monitor, speakers, files motors, limbs
Ch2 Intelligent Agents (input, output, Types, ……)

18. AI Complex?

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The Roots of AI
AI has roots in a number of older sciences , particularly:
•Philosophy
•Logic/Mathematics
•Computation
•Psychology/Cognitive Science
•Biology/Neuroscience
•Evolution
•Bylookingateachoftheseinturn,wecangainabetter
understandingoftheirroleinAI,andhowtheseunderlyingthe
developedtoplaythatrole.

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History of AI: 1952-1969
•Great successes!
–Solving hard math problems
–game playing
–LISP was invented by McCarthy (1958)
–McCarthy went to MIT and Marvin Minsky started lab at
Stanford (Both powerhouses in AIto this day)
History of AI: 1966 -1973
•Reality
–Systems fail to play chess and translate Russian
–neural networkswas exposed (neural networksdid not return
to appear until late 1980s)

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AI History: 1969 -1979
•Knowledge-based Systems (Expert systems)
–Problem: General logical algorithms could not be applied to
realistic problems
–Solution: accumulate specific logical algorithms
•DENDRAL –infer chemical structure
•AI History: 1987 -2000
•AI becomes a science
–More repeatability of experiments
–More development
•Intelligent Agents (1994)
–AIsystems exist in real environments with real sensory inputs

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2000-Where are We Now?
–Autonomousplanning:schedulingoperationsaboardarobot
–Gameplaying:KasparovlosttoIBM’sBigBlueinchess
–AutonomousControl:CMU’sNAVLABdrovefromPittsburghto
SanFranciscoundercomputercontrol98%oftime
–Stanfordvehiclewins 2006 DARPA Grand Challenge
CMU’s2005vehiclefallscrashesatstartingline
–Logistics:organizedthetimetablesforanytask.
–Robotics:remoteheartoperations.
–humangenome,proteinfolding,drugdiscovery.
–stockmarket…………………… .etc.

25. 1. AI Application in E-Commerce
Recommendation engines , chatbots help improve the
user experience , Credit card fraud and fake reviews.
2. Applications Of Artificial Intelligence in
Education
Creating Smart Content, Voice Assistants,
Personalized Learning
3. Applications of Artificial Intelligence in Lifestyle
Autonomous Vehicles, Spam Filters, Facial
Recognition, Recommendation System
4. Applications of Artificial Intelligence in
Navigation
GPS technology can provide users with accurate,
timely, and detailed information to improve safety.
5. Applications of Artificial Intelligence in Robotics
: Carrying goods in hospitals, factories, and
warehouses, Cleaning offices and large equipment
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6. Applications ofArtificial Intelligence in
Human Resource
job candidates' profiles
7. Applications of Artificial Intelligence in
Healthcare
detect diseases and identify cancer cells,
analyze chronic conditions ,early diagnosis.
8. Applications of Artificial Intelligence in
Agriculture
computer vision, robotics
9. Applications of Artificial Intelligence in
Automobiles
self-driving
.
10. Applications of Artificial Intelligence in
Social Media
determine what posts you are shown.
DeepTextcan understand conversations better.
11. Applications of Artificial Intelligence in
Marketing
•Data mining
2000-Where are We Now?

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31. 3
1
Genetic Algorithms
•Basic scheme
–(1)Initialize population
–(2)evaluate fitnessof each member
–(3)Selection of the best Chromosomes
–(4) Crossover
–(5) introduce random mutationsin new
generation
–Continue (2)-(3)-(4) until prespecified
number of generations are complete
•Start with krandomly generated states
(population)
•Evaluation function (fitness function).
Higher values for better states.
•Produce the next generation of states by
selection, crossover, and mutation

32. A Simple Example
The Traveling Salesman Problem:
Find a tour of a given set of cities so that
–each city is visited only once
–the total distance traveled is minimized
Representation
Representation is an ordered list of city
numbers known as an order-basedGA.
1) London 3) Dunedin 5) Beijing 7) Tokyo
2) Venice 4) Singapore 6) Phoenix 8) Victoria
CityList1 (3 5 7 2 1 6 4 8)
CityList2 (2 5 7 6 8 1 3 4)

33. TSP Example: 30 Cities0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
y
x

34. Solution
i(Distance = 941)0
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60
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120
0 10 20 30 40 50 60 70 80 90 100
y
x
TSP30 (Performance = 941)

35. Solution
j(Distance = 800)44
62
69
67
78
64
62
54
42
50
40
40
38
21
35
67
60
60
40
42
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99
0
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y
x
TSP30 (Performance = 800)

36. Solution
k(Distance = 652)0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
y
x
TSP30 (Performance = 652)

37. Best Solution (Distance = 420)42
38
35
26
21
35
32
7
38
46
44
58
60
69
76
78
71
69
67
62
84
94
0
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60
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100
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0 10 20 30 40 50 60 70 80 90 100
y
x
TSP30 Solution (Performance = 420)

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Computer Vision: The world is composed of three-dimensional objects, but
the inputs to the human eye and computers' TV cameras are two dimensional.
Some useful programs can work in two dimensions, but full computer vision requires
partial three-dimensional information that is not just a set of two-dimensional views.
At present there are only limited ways of representing three-dimensional information
directly, and they are not as good as what humans evidently use.

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ThePoseidonsystemisbasedona
networkofoverheadandunderwater
camerasinstalledinapublicpool.
alllinkedtoacomputersystemthatis
goingtoacquirevideosignalsinreal-time
filtersthemextractshumanbodyshapes
fromimagesandassessesthemovementof
thesebodies.
Wheneverthesystemdetectsthatabody
movementpattern(orlackthereof)
resemblesoneofadrowningswimmer,it
sendsanalerttolifeguardsthrough
pagersthatindicatethelocationofthe
endangeredperson.
Computer Vision applied System

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•Inthe1990s,computerspeechrecognitionreachedapractical
levelforlimitedpurposes.ThusUnitedAirlineshasreplacedits
keyboardtreeforflightinformationbyasystemusingspeech
recognitionofflightnumbersandcitynames.Itisquite
convenient.
Speechrecognitionapplication
•Telephone-basedInformation(directions, air travel, banking, etc)
•Hands-free (in car)
•Secondlanguage('L2') (accentreduction)
•Audio archive searching
Speech recognition

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Complex example used speech recognition

46. •The process of building expert systems is often called knowledge engineering.
The knowledge engineer is involved with all components of an expert system:
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Expert Systems
Building expert systems is generally an iterative process. The components and their
interaction will be refined over the course of numerous meetings of the knowledge
engineer with the experts and users. We shall look in turn at the various components.

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Goal:Tocreatecomputationalmodelsoflanguageinenoughdetail
thatyoucouldwritecomputerprogramstoperformvarioustasks
involvingnaturallanguage.
Scientific:toexplorethenatureoflinguisticcommunication
Practical:toenableeffectivehuman-machinecommunication
Just getting a sequence of words into a computer is not enough.
Parsing sentences is not enough either.
The computer has to be provided with an understanding of the
domain the text is about, and this is presently possible only for
very limited domains.
Understanding Natural Language

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AI Branches
RepresentationKnowledgeneedstoberepresentedsomehow–perhapsasa
seriesofif-thenrules,asaframebasedsystem,asasemanticnetwork,orinthe
connectionweightsofanartificialneuralnetwork.
LearningAutomaticallybuildingupknowledgefromtheenvironment–suchas
acquiringtherulesforarulebasedexpertsystem,ordeterminingtheappropriate
connectionweightsinanartificialneuralnetwork.
(Detailedinnextchapters)
RulesThesecouldbeexplicitlybuiltintoanexpertsystembyaknowledge
engineer,orimplicitintheconnectionweightslearntbyaneuralnetwork.
SearchThiscantakemanyforms–perhapssearchingforasequenceofstates
that
leadsquicklytoaproblemsolution,orsearchingforagoodsetofconnection
weightsforaneuralnetworkbyminimizingafitnessfunction.

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Game playing
Game playing is a search problem Defined by:
–Initial state –Successor function
–Goal test –Path cost / utility / payoff function
Characteristics of game playing:
•Initial state: initial board position and player
•Operators: one for each legal move
•Terminal states: a set of states that mark the end of the game
•Utility function: assigns numeric value to each terminal state
•Game tree: represents all possible game scenarios

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(Our) Basis of Game Playing: Search for best move
every time
Initial Board State Board State 2 Board State 3
Board State 4 Board State 5
Search for Opponent
Move 1 Moves 2
Search for Opponent
Move 3 Moves

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May, 1997: Deep Blue beats the World Chess Champion
I could feel human-level intelligence across the room
vs.
Youcanbuymachinesthatcanplaymasterlevelchessforafew
hundreddollars.ThereissomeISinthem,buttheyplaywell
againstpeoplemainlythroughbruteforcecomputation
lookingathundredsofthousandsofpositions.Tobeataworld
championbybruteforceandknownreliableheuristicsrequires
beingabletolookat200millionpositionspersecond.

58. What Can AI Do? From these examples
•Play a game of table tennis?
•Drive safely along a road with signals?
•Drive safely along any road?
•Buy a week's worth of groceries on the web?
•Buy a week's worth of groceries at Berkeley Bowl?
•Discover and prove a new mathematical theorem?
•Converse successfully with another person for an hour?
•Perform a complex surgical operation?
•Unload a dishwasher and put everything away?
•Translate spoken English into spoken Arabic in real time?
•Write an intentionally funny story?
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